[ { "id": "T01", "target": "bestpractice_pdf_to_markdown", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "### Cause of the Crash\n\nOn Apple Silicon macOS, Docling's layout detection uses the **RT-DETR V2** model, which executes operations using **`torch.float64`** (double-precision floating point). \n\nApple's MPS (Metal Performance Shaders) backend in PyTorch does not support `torch.float64` for these operations, resulting in an unhandled backend crash during the layout detection phase.\n\n---\n\n### Workaround\n\nTo work around this, force Docling to run on the CPU rather than the MPS backend by passing the **`--device cpu`** flag:\n\n```bash\ndocling input.pdf --to md --device cpu --output output_dir\n```\n\n*(Optional)* For text-only PDFs that do not require OCR, you can also add `--no-ocr` to skip unnecessary OCR model pathways:\n\n```bash\ndocling input.pdf --to md --device cpu --no-ocr --output output_dir\n```\n\n> **Note:** Relying on the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` is not recommended, as it frequently fails to take effect across various PyTorch version combinations; explicitly specifying `--device cpu` is the deterministic solution.", "error": null, "opened": [ "bestpractice_pdf_to_markdown" ], "requested": [ "bestpractice_pdf_to_markdown" ], "opened_target": true, "prompt_tokens": 14051, "completion_tokens": 588, "sec": 7.8223559856414795, "cost": 0.012743250000000001, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T02", "target": "mac_universal_clipboard", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "Yes. The specific Keychain item responsible for resetting your Mac's local Handoff identity key is:\n\n```text\nhandoff-own-encryption-key\n```\n\nDeleting this item forces macOS to regenerate the local encryption key used for Continuity/Handoff syncing without having to sign out and back in to iCloud.\n\n---\n\n### Step-by-Step Instructions\n\n#### Option 1: Delete via Keychain Access (GUI)\n1. Open **Keychain Access** (`/Applications/Utilities/Keychain Access.app`).\n2. In the search bar at the top right, search for:\n ```text\n handoff-own-encryption-key\n ```\n3. Right-click the item and select **Delete \"handoff-own-encryption-key\"**.\n4. Confirm deletion if prompted for your Mac administrator password.\n\n#### Option 2: Delete via Terminal (CLI)\nYou can also delete it using the macOS `security` CLI:\n\n```bash\nsecurity delete-generic-password -l \"handoff-own-encryption-key\" 2>/dev/null\n```\n\n---\n\n### Restart the Handoff Daemons\n\nAfter deleting the key, restart the Continuity daemons so macOS recreates the key and refreshes the clipboard state immediately:\n\n```bash\ndefaults delete ~/Library/Preferences/com.apple.coreservices.useractivityd.plist ClipboardSharingEnabled 2>/dev/null\ndefaults write ~/Library/Preferences/com.apple.coreservices.useractivityd.plist ClipboardSharingEnabled 1\nkillall useractivityd 2>/dev/null\nkillall sharingd 2>/dev/null\nkillall pboard 2>/dev/null\nsleep 2\npgrep -fl 'useractivityd|sharingd|pboard'\n```\n\nOnce the processes reappear, test copying and pasting in both directions (iPhone/iPad $\\rightarrow$ Mac and Mac $\\rightarrow$ iPhone/iPad).", "error": null, "opened": [ "mac_universal_clipboard" ], "requested": [ "mac_universal_clipboard" ], "opened_target": true, "prompt_tokens": 12657, "completion_tokens": 536, "sec": 8.640630722045898, "cost": 0.011502749999999999, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T03", "target": "openreview", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "To resolve the HTTP 403 error caused by an expired token, follow these steps:\n\n1. **Delete the cached token file:**\n ```bash\n rm tmp/openreview_token.json\n ```\n2. **Re-run your OpenReview CLI query.** \n\nThe tool will authenticate using your credentials (`openreview_email` / `openreview_password` in `.env` or `OPENREVIEW_EMAIL` / `OPENREVIEW_PASSWORD` environment variables), generate a fresh token, and cache it again.\n\n*(Note: OpenReview enforces a login rate limit of at most 3 logins per time window, so avoid repeated login attempts beyond retrying once).*", "error": null, "opened": [ "openreview" ], "requested": [ "openreview" ], "opened_target": true, "prompt_tokens": 13320, "completion_tokens": 382, "sec": 18.42257785797119, "cost": 0.0114225, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T04", "target": "workflow_bilibili_whisper_transcription", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "When downloading audio tracks from Bilibili in batches, maintain a **2 to 3 second interval** between successive downloads. \n\n### Key Recommendations to Avoid Anti-Scraping Triggers:\n1. **Single-Threaded Execution**: Download audio tracks sequentially (one by one) rather than concurrently.\n2. **2–3 Second Delay**: Introduce an explicit sleep of 2–3 seconds between each download request.\n3. **Set Request Headers**: Ensure standard browser headers are included (e.g., valid `User-Agent` and `Referer: https://www.bilibili.com/`) to prevent `352` (blocked/risk control) or `412` (precondition failed) errors.\n4. **Target Audio Only**: Download only the required audio stream/format to minimize bandwidth and request overhead.\n5. **Error & Retry Handling**: Record failed video IDs to retry separately rather than aggressively retrying in an immediate tight loop.", "error": null, "opened": [ "workflow_bilibili_whisper_transcription", "download_and_transcribe" ], "requested": [ "workflow_bilibili_whisper_transcription", "download_and_transcribe" ], "opened_target": true, "prompt_tokens": 16180, "completion_tokens": 636, "sec": 12.990039348602295, "cost": 0.014519999999999998, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T05", "target": "playwright_ajax_capture", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "When reverse-engineering internal APIs, this pattern typically happens because the API processes creation asynchronously (or decouples ingestion from persistence), returning an async tracking token or client-side temporary UUID (`creation_uuid`) rather than the finalized database ID.\n\nTo reliably obtain the real resource ID, follow the **POST-then-GET reconciliation pattern**:\n\n---\n\n### 1. Do Not Use the Immediate Response for the ID\n- Never pass `creation_uuid` as the resource `id` (or assume a fallback `.get(\"id\")` will work).\n- Passing `None` or an invalid UUID into subsequent API calls (such as `parent_message_id` for thread replies) often silently degrades the operation (e.g., a thread reply turns into a standalone message).\n\n---\n\n### 2. Follow Up with a List / Fetch Query (GET)\nImmediately after the `POST` request succeeds (status `200`, `201`, or `202`):\n1. **Call the corresponding list or query endpoint** (e.g., list messages, list posts, or list resources for that container/channel).\n2. **Locate the newly created record by content matching**:\n - Match by unique content text or a distinct payload marker (e.g., exact text body, title, or unique timestamp/nonce).\n - In some APIs, the list endpoint may echo back client-provided identifiers or `creation_uuid` inside the record metadata.\n3. **Extract the real ID** (usually a numeric or UUID primary key `id`) from the matched item.\n\n---\n\n### 3. Handle Eventual Consistency / Propagation Delays\nBecause creation is asynchronous:\n- If the first `GET` does not return the item immediately, implement a **bounded poll / retry loop** (e.g., sleep 0.5–1.0s, retry up to 3–5 times).\n- Once the matched item appears in the list, extract its real ID and cache or pass it forward to downstream operations.\n\n---\n\n### Example Implementation Pattern (Python)\n\n```python\nimport time\nimport requests\n\n# 1. Create the resource\ncreate_resp = session.post(\n \"https://example.com/api/v1/posts\",\n json={\"body\": post_content, \"creation_uuid\": client_uuid},\n)\ncreate_resp.raise_for_status()\n\n# 2. Reconcile via GET to find the real ID\nreal_id = None\nfor _ in range(5):\n time.sleep(0.5) # brief wait for server-side persistence\n list_resp = session.get(\n \"https://example.com/api/v1/posts\", params={\"limit\": 10}\n )\n if list_resp.status_code == 200:\n items = list_resp.json().get(\"posts\", [])\n # Match by content or client uuid\n match = next(\n (\n item\n for item in items\n if item.get(\"creation_uuid\") == client_uuid\n or item.get(\"body\") == post_content\n ),\n None,\n )\n if match and match.get(\"id\"):\n real_id = match[\"id\"]\n break\n\nif not real_id:\n raise RuntimeError(\"Failed to resolve real ID for newly created resource\")\n\n# 3. Proceed with downstream operations using real_id\nsession.post(\n \"https://example.com/api/v1/comments\",\n json={\"parent_id\": real_id, \"body\": \"Follow-up action\"},\n)\n```", "error": null, "opened": [ "playwright_ajax_capture" ], "requested": [ "playwright_ajax_capture" ], "opened_target": true, "prompt_tokens": 16185, "completion_tokens": 1616, "sec": 25.47685480117798, "cost": 0.01819875, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T06", "target": "workflow_watchdog", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "The recommended default wake-up interval is **about 30 minutes (1,800 seconds)**.\n\n### How to Implement and Manage the Check-in\n\n1. **Do not wait in place**:\n * After dispatching background tasks, set an inspection timer (e.g., using `ScheduleWakeup` in the Claude Code harness, or one-shot delayed execution via Process Launcher `POST /run` with `delay_seconds: 1800` in other harnesses).\n\n2. **Evaluate on wake-up**:\n Inspect real task indicators (output files, logs, exit statuses) and decide between two states:\n * **Genuinely busy**: The sub-agent is actively producing new output or progressing through steps. Leave it alone and schedule another wake-up interval.\n * **Stuck in a loop**: No observable progress over an extended window, or the same step/error is retrying repeatedly. Kill the process (e.g., `TaskStop`), and proceed with partial results or take an alternative approach.\n\n3. **Autonomous orchestration**:\n * Deciding whether a task is \"genuinely busy\" vs. \"stuck\" and taking action (rescheduling or killing) is a technical orchestration decision that should be carried through directly rather than pausing to prompt the user.", "error": null, "opened": [ "workflow_watchdog", "skill_agentic_patrol", "workflow_parallel_subagents" ], "requested": [ "workflow_watchdog", "skill_agentic_patrol", "workflow_parallel_subagents" ], "opened_target": true, "prompt_tokens": 18039, "completion_tokens": 789, "sec": 10.406167268753052, "cost": 0.016488000000000003, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T07", "target": "workflow_parallel_subagents", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "You should not default to setting `temperature: 0` on subagents for stability for several key reasons:\n\n1. **Risk of Pathological Repetition**: Forcing strict determinism on longer text or free-form outputs frequently triggers repetitive loops, degenerate prose, and repetitive phrasing rather than cleaner generation.\n2. **Provider and Architecture Defaults**: Modern providers and models often have their own fine-tuned sampling strategies. Many models (such as DeepSeek/DS4 or modern reasoning models) already implement deterministic handling for structured/protocol layers or rely on specific provider-side defaults to function properly.\n3. **Loss of Parallel Exploration Value**: A primary purpose of running parallel subagents—especially in research, brainstorming, code review, or cross-validation—is independent exploration and diverse angle coverage. Forcing `temperature: 0` collapses sampling diversity, reducing the subagents' ability to catch blind spots or provide genuine cross-checking.\n\n**Best Practice:**\nDefault to **not specifying a temperature**, allowing the model/provider or server configuration to apply its recommended defaults. Only set a fixed temperature when you have an explicit, verified requirement that a specific model needs fixed sampling for a narrow task.", "error": null, "opened": [ "workflow_parallel_subagents" ], "requested": [ "workflow_parallel_subagents" ], "opened_target": true, "prompt_tokens": 15564, "completion_tokens": 672, "sec": 13.049737453460693, "cost": 0.014193, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T08", "target": "bestpractice_markdown_html_conversion", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "The recommended padding to apply to the `body` or main container is **24px or more** (e.g., `padding: 24px;` or `padding: 1.5rem;`).\n\n### Implementation Example\n\nIn your CSS or inline styling:\n\n```css\nbody, \nmain {\n box-sizing: border-box;\n padding: 24px;\n}\n```\n\nOr scoped using a media query for mobile screens:\n\n```css\n@media (max-width: 768px) {\n body,\n .container {\n padding-left: 24px;\n padding-right: 24px;\n }\n}\n```\n\nThis prevents the rendered Markdown content from hugging the viewport edges on mobile devices and provides comfortable reading margins.", "error": null, "opened": [ "bestpractice_markdown_html_conversion" ], "requested": [ "bestpractice_markdown_html_conversion" ], "opened_target": true, "prompt_tokens": 12350, "completion_tokens": 408, "sec": 8.816166400909424, "cost": 0.0107925, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T09", "target": "codex_cli", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "To disable `web_search` in Codex CLI when targeting a custom OpenAI-compatible backend:\n\n### 1. Configuration Key and File Location\n\nAdd the setting to your base configuration file (`$CODEX_HOME/config.toml`) or to the specific profile file loaded for the run:\n\n```toml\nweb_search = \"disabled\"\n```\n\n### 2. Required Value Type\n\n* **Type**: **String** (specifically the string unit variant `\"disabled\"`).\n* **Important**: Do **not** use a boolean (`false`, `true`) or keywords like `off`/`on`/`enabled`. The parser rejects booleans and other variants with an error such as:\n ```text\n expected string only in 'web_search'\n ```\n Only the string `\"disabled\"` is accepted.\n\n### 3. Syntax and Placement Rule\n\n* In TOML syntax, any key-value pair placed after a section header (`[section]`) is scoped inside that table.\n* Therefore, `web_search = \"disabled\"` must be placed as a **bare top-level key above any `[section]` header** in the file.", "error": null, "opened": [ "opencode_cli", "codex_cli", "skill_ai_agent_cli" ], "requested": [ "opencode_cli", "codex_cli", "skill_ai_agent_cli" ], "opened_target": true, "prompt_tokens": 16037, "completion_tokens": 933, "sec": 11.636162996292114, "cost": 0.015526499999999999, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T10", "target": "apple_photos", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "The maximum number of image pairs allowed in a single delete manifest is **50 pairs**. \n\nLarger sets must be split into independent plans, each requiring fresh review and authorization.", "error": null, "opened": [ "apple_photos" ], "requested": [ "apple_photos" ], "opened_target": true, "prompt_tokens": 13690, "completion_tokens": 234, "sec": 8.293676137924194, "cost": 0.011144999999999999, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T11", "target": "colab", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "In `google-colab-cli` version 0.7.4, an unrecognized `--gpu` value is not validated or rejected locally. Instead, the CLI defaults to sending the request as **`A100`**.\n\nBecause of this behavior:\n- The command will attempt to provision an A100 runtime instead of failing with an argument validation error.\n- Always probe and record the actual allocated hardware using:\n ```bash\n nvidia-smi --query-gpu=name,compute_cap,memory.total\n ```\n to verify what hardware Colab actually assigned to your session.", "error": null, "opened": [ "colab" ], "requested": [ "colab" ], "opened_target": true, "prompt_tokens": 17228, "completion_tokens": 417, "sec": 9.339493989944458, "cost": 0.01448475, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T12", "target": "dataforseo", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "### Cause of the Issue\n\nError code **`40104`** occurs because your DataForSEO account credentials are authenticated, but the **account has not completed account verification**. \n\nIn DataForSEO's API permission hierarchy:\n- Basic account management endpoints (such as `balance`) can still succeed for unverified accounts.\n- Keyword data endpoints—even when targeting the sandbox environment (`https://sandbox.dataforseo.com`)—are blocked until account verification is finalized.\n\n---\n\n### How to Fix It\n\n1. **Log in to DataForSEO:** \n Go to the [DataForSEO Dashboard/Panel](https://app.dataforseo.com/).\n\n2. **Complete Required Verification:** \n - Check for any pending verification banners or prompts in your dashboard (e.g., verifying your registered email address, confirming phone number, or filling out required business/profile details).\n - Follow the prompts to complete the verification requirements.\n\n3. **Retry the Query:** \n Once the account status updates to verified, test the keyword endpoints again:\n ```bash\n # Sandbox test\n .venv/bin/dataforseo-skill --base-url https://sandbox.dataforseo.com keyword-suggestions \"agent harness\" --limit 3\n\n # Live endpoint\n .venv/bin/dataforseo-skill keyword-suggestions \"agent harness\" --limit 10\n ```", "error": null, "opened": [ "dataforseo" ], "requested": [ "dataforseo" ], "opened_target": true, "prompt_tokens": 12569, "completion_tokens": 675, "sec": 8.627060890197754, "cost": 0.011958, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T13", "target": "firewalla", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "You need to specify the **`--execute`** flag.\n\n### Why it is needed:\nBy default, the `firewalla-skill` CLI operates in **dry-run mode** to prevent accidental network queries or unexpected operations. To explicitly query and pull live data from your physical Firewalla device, you must include `--execute`.\n\n### Example usage:\n```bash\n# Query live device inventory\nfirewalla-skill devices --execute --all --json --output reports/devices_all_latest.json\n\n# Query live alarms\nfirewalla-skill alarms --execute --since-days 3 --include-archive --all --json --output reports/alarms_last3d_latest.json\n```\n\n*(Note: Ensure you are running commands from the skill's environment with your `.venv` activated and your SSH alias/credentials configured in `.firewalla.local.json` or through environment variables).*", "error": null, "opened": [ "firewalla" ], "requested": [ "firewalla" ], "opened_target": true, "prompt_tokens": 12809, "completion_tokens": 284, "sec": 23.469752073287964, "cost": 0.01067175, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T14", "target": "apple_calendar", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "### 1. Why `~/Library/Calendars/Calendar.sqlitedb` Failed\n\nOn modern versions of macOS, **`~/Library/Calendars/Calendar.sqlitedb` is a 0-byte decoy file**. It contains no schema or data, so querying it will always return empty results without raising an error.\n\n---\n\n### 2. The Path You Should Actually Use\n\nThe real live EventKit database is located in Apple Calendar's App Group container:\n\n```text\n~/Library/Group Containers/group.com.apple.calendar/Calendar.sqlitedb\n```\n\n---\n\n### 3. Critical Steps for Querying Safely and Accurately\n\nWhen implementing your script, keep these three details in mind:\n\n1. **Copy the WAL and SHM sidecars too:**\n SQLite operates in WAL (Write-Ahead Logging) mode. Recent or uncheckpointed changes live in `-wal` and `-shm` files. Always copy all three files together into your temporary directory:\n - `Calendar.sqlitedb`\n - `Calendar.sqlitedb-wal` (if present)\n - `Calendar.sqlitedb-shm` (if present)\n\n2. **Open as read-only:**\n Never query the live database directly. Open your copied snapshot using `mode=ro` (e.g., `sqlite3 \"file:/path/to/snapshot/Calendar.sqlitedb?mode=ro\"`).\n\n3. **Time epoch reference trap:**\n Apple stores `start_date` and `end_date` as seconds since the **Apple/Cocoa epoch (`2001-01-01 00:00:00 UTC`)**, *not* the standard Unix epoch (`1970-01-01`). To convert to Unix time, add `978307200` seconds.", "error": null, "opened": [ "apple_calendar" ], "requested": [ "apple_calendar" ], "opened_target": true, "prompt_tokens": 14229, "completion_tokens": 829, "sec": 13.261405944824219, "cost": 0.013780500000000001, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T15", "target": "skill_image_generation", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "When generating infographics with GPT Image 2.5, you should use a **draft-and-repaint (image-to-image) workflow** rather than prompting from text alone.\n\n---\n\n### The Recommended Workflow\n\n1. **Generate a structural draft locally first**:\n * Create an initial chart or diagram draft programmatically (e.g., using `matplotlib`).\n * Ensure core titles, annotations, data points, and structural layout are already in place on the draft image.\n2. **Pass the draft as an input image (`-i`)**:\n * Feed the draft into GPT Image 2.5 using an image-editing command:\n ```bash\n generate-image -i draft.png -p \"Redraw this into a polished, modern infographic...\" -o final_infographic.png --model gpt-image-2.5-sunburst --aspect-ratio 16:9\n ```\n * Use `--aspect-ratio 16:9` for standard horizontal infographics.\n3. **Configure execution timeouts**:\n * Set an explicit command timeout of 300–420 seconds (e.g., `timeout=300000` ms), as complex redraw operations take significantly longer than standard generations and will fail under default 120s bash timeouts.\n\n---\n\n### Why This Workflow Is Necessary\n\n* **GPT Image functions best as a repaint tool, not a ground-up generator**: Generating detailed infographics from text prompts alone produces unreliable structures, inconsistent layouts, and hallucinated or garbled information.\n* **Layout and text fidelity**: When provided with a concrete input image, GPT Image 2.5 accurately grasps the spatial composition and layout, reliably preserves the existing title and annotation text, and focuses its capabilities on elevating the visual design, styling, and rendering quality.", "error": null, "opened": [ "skill_image_generation" ], "requested": [ "skill_image_generation" ], "opened_target": true, "prompt_tokens": 13681, "completion_tokens": 1038, "sec": 12.732460975646973, "cost": 0.014153250000000001, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T16", "target": "skill_intake", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "The sync step reads only two audio file extensions:\n\n* **`.m4a`**\n* **`.qta`** (converted to `.m4a` via `ffmpeg`)\n\nAny other audio format (such as `.wav`, `.mp3`, etc.) is completely ignored.\n\n---\n\n### Additional Reasons Recordings May Be Skipped:\n\nIf your skipped files are indeed `.m4a` or `.qta`, check the following:\n\n1. **Already Synced**: The intake tool skips existing destination files rather than overwriting them (`data/YYYYMMDD/YYYYMMDD_HHMM_watch.m4a`).\n2. **Date Filtering**: If running sync for a specific date (e.g., `--date YYYYMMDD`), recordings from other days are omitted.\n3. **Missing `ffmpeg`**: If your source contains `.qta` files, `ffmpeg` must be installed and available in your `PATH` to convert them; run `python -m intake_skill doctor` to verify that `ffmpeg_available` is `true`.", "error": null, "opened": [ "skill_intake" ], "requested": [ "skill_intake" ], "opened_target": true, "prompt_tokens": 15682, "completion_tokens": 647, "sec": 9.33602499961853, "cost": 0.014187749999999999, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T17", "target": "watch_development", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "By default, watchOS does not expose the Developer Mode toggle until the watch has established an initial development connection with a Mac running Xcode. \n\nFollow these steps to reveal and enable Developer Mode:\n\n### 1. Ensure Host Connectivity via the Paired iPhone\n* The Apple Watch does not communicate directly over its magnetic USB charging cable (that cable only supplies power). It connects to the Mac exclusively through your paired iPhone.\n* **Connect your paired iPhone to your Mac** via USB (or ensure both are trusted on the same Wi-Fi network).\n* Ensure **Wi-Fi and Bluetooth** are turned on for both the iPhone and the Apple Watch.\n* Keep the Apple Watch unlocked and on its charger near the iPhone.\n\n### 2. Pair the Watch to Xcode (Device Hub)\n1. On your Mac, open Xcode.\n2. Open **Device Hub** by navigating to:\n * **Xcode > Open Developer Tool > Device Hub** (or via the run destination menu: **Manage Devices…**).\n3. In Device Hub, click **\"+\"** and select **\"Pair Nearby Device\"** to initiate pairing with your Apple Watch.\n4. If prompted with a numeric pairing code on the watch face, confirm the code to establish trust.\n\n### 3. Restart the Apple Watch\n1. Once the watch has been recognized or paired in Device Hub at least once, **restart the Apple Watch** (press and hold the side button, then slide the power off slider; turn it back on after a few seconds).\n\n### 4. Enable Developer Mode\n1. After the watch reboots, open **Settings > Privacy & Security** on the Apple Watch.\n2. Scroll to the bottom; the **Developer Mode** option will now be visible.\n3. Tap **Developer Mode**, turn the switch **On**, and follow the on-screen prompt to restart the watch once more to activate it.", "error": null, "opened": [ "watch_development" ], "requested": [ "watch_development" ], "opened_target": true, "prompt_tokens": 14459, "completion_tokens": 811, "sec": 10.597048044204712, "cost": 0.013885499999999999, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T18", "target": "koyeb", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "### Why Not to Rely on the Standard CLI (`metrics get`)\n\nYou should not rely on the standard Koyeb CLI (`koyeb metrics get`) for zero-traffic automation because **the native CLI flattens and maps raw upstream `null` values to `0`**. \n\nIn telemetry, a `null` value indicates missing, uncollected, or unmeasured data points rather than confirmed zero traffic. Because the standard CLI transforms `null` into `0`, automation relying on it can produce false positives—treating missing telemetry or unmonitored windows as verified zero-traffic periods, which risks premature or unsafe scale-down.\n\n---\n\n### What Script to Use Instead\n\nUse the dedicated lossless reader script: **`scripts/request_metrics.py`**.\n\nThis script preserves raw null request telemetry (distinguishing true `0` from `null`), maintains per-series labels, and provides accurate three-state classification needed for scale-to-zero evaluation.\n\n#### Example Usage\n```bash\npython3 scripts/request_metrics.py \\\n --service-id \\\n --start 2026-01-01T00:00:00Z \\\n --end 2026-01-01T01:00:00Z \\\n --step 1h\n```", "error": null, "opened": [ "koyeb" ], "requested": [ "koyeb" ], "opened_target": true, "prompt_tokens": 14472, "completion_tokens": 733, "sec": 11.363449811935425, "cost": 0.01360275, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T19", "target": "narwal", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "In `narwal-local`, `is_docked` and `charging_state` are **always emitted as `null` by design**. The CLI does not attempt to normalize these fields because the underlying raw status codes vary significantly across firmware versions.\n\nTo reliably determine whether the vacuum is docked and charging using the CLI, follow these steps:\n\n---\n\n### 1. Primary Method: Monitor the Battery Trend\n\nThe definitive way to confirm that the robot is docked and charging is to verify whether **`battery_percent` is increasing over time**:\n\n* **Option A: Use `watch` to stream live telemetry**\n Run a bounded observation window (maximum 300 seconds):\n ```bash\n narwal-local watch --duration 60\n ```\n Inspect the output lines in the resulting JSONL stream. If `battery_percent` trends upward across the window, the vacuum is actively charging on the dock.\n\n* **Option B: Compare two snapshots spaced apart**\n Take two snapshots a few minutes apart:\n ```bash\n narwal-local snapshot\n # wait 2–5 minutes\n narwal-local snapshot\n ```\n Compare `data.base_query.battery_percent`. An increasing value confirms charging.\n\n---\n\n### 2. Inspect Raw Telemetry Codes\n\nWhile the high-level semantic fields remain `null`, the raw status payloads are preserved:\n* Check `dock_raw` and `charging_status_raw` in the snapshot JSON.\n* Although exact values depend on your specific model and firmware version, non-null values or transitions in these raw structures can be correlated with your device's docked/undocked states.\n\n---\n\n### 3. Supporting Context Clues\n\n* **Base Mode & Task Status:** \n Inspect `data.base_query.mode`. When docked and idle, the robot will generally report `semantic: \"standby\"` with null task metrics (`area_m2: null`, `elapsed_seconds: null`).\n* **Clean Readiness Check (Non-destructive):**\n The Narwal protocol only allows cleaning tasks to begin while the vacuum is docked. Running a dry-run clean inspection:\n ```bash\n narwal-local start --dry-run --yes\n ```\n If the robot is undocked, it declines the command with `NOT_READY` (exit code `20`).", "error": null, "opened": [ "narwal" ], "requested": [ "narwal" ], "opened_target": true, "prompt_tokens": 16213, "completion_tokens": 1313, "sec": 12.593303918838501, "cost": 0.0170835, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T20", "target": "skill_opencode_data", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "### Why Subagent Fan-Out Sessions Are Missing\n\nWhen exporting OpenCode sessions to Markdown files (via `opencode_skill export`):\n\n1. **Default Exclusion Flag**: By default, the exporter filters out subagent fan-out sessions to keep the exported archive focused on top-level interactive sessions. They are deliberately skipped during the export run unless explicitly requested.\n2. **Turn Requirement**: The export process only writes markdown files for sessions that have at least one user turn (`## User`). Subagent child sessions that only receive automated system prompts or subagent instructions without standard user turns may not qualify under standard export criteria.\n\n---\n\n### How to Include Them\n\nTo include subagent fan-out sessions in your export:\n\n1. **Add the `--include-subagent` flag**:\n Run the export command passing `--include-subagent`:\n ```bash\n .venv/bin/python -m opencode_skill export --out --include-subagent\n ```\n\n2. **Combine with timeframe filters as needed**:\n If filtering by time (e.g., sessions from the last 30 days or from a specific date), combine `--include-subagent` with `--since`:\n ```bash\n .venv/bin/python -m opencode_skill export --out --since 30d --include-subagent\n ```\n\n3. **Verify via Dry Run**:\n You can verify how many total sessions (including subagents) will be exported before writing to disk using `--dry-run`:\n ```bash\n .venv/bin/python -m opencode_skill export --out --include-subagent --dry-run\n ```\n\n*(Note: Treat exported markdown files as private, as they contain full prompt and message bodies and should not be checked into public version control).*", "error": null, "opened": [ "ai_session_export", "skill_opencode_data" ], "requested": [ "ai_session_export", "skill_opencode_data" ], "opened_target": true, "prompt_tokens": 15748, "completion_tokens": 927, "sec": 24.464330434799194, "cost": 0.015287249999999999, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T21", "target": "speaker_notes", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "The strict upper limit (hard gate) is **22 words** per ordinary spoken sentence.\n\nKey sentence-length rules from the speaker notes guidelines:\n- **Target range**: Keep ordinary spoken sentences between **12 and 20 words** so they are easy to say in one breath.\n- **Hard gate**: No ordinary spoken sentence may exceed **22 words**.\n- **Payload limit**: A single sentence may carry at most one main payload and one supporting clause; if it combines a condition, an exception, and a consequence, it must be split.", "error": null, "opened": [ "speaker_notes", "copy_workflow" ], "requested": [ "speaker_notes", "copy_workflow" ], "opened_target": true, "prompt_tokens": 16094, "completion_tokens": 456, "sec": 8.324907064437866, "cost": 0.0137805, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T22", "target": "skill_semantic_search", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "To handle relative paths in your file list with `semantic-search`:\n\n* **Command-line option to define the base directory:** \n Use **`--source-root `** (e.g., `--source-root /path/to/base/dir`).\n\n* **Default behavior if omitted:** \n The tool resolves relative entries against the **CLI's current working directory** (where the command is being executed), *not* the directory where the file list file itself is located or was generated. \n\n*(Note: If the CLI is run from a different directory without specifying `--source-root`, the resolved paths will not match cached identities, causing queries to silently return empty results without error.)*", "error": null, "opened": [ "skill_semantic_search" ], "requested": [ "skill_semantic_search" ], "opened_target": true, "prompt_tokens": 13509, "completion_tokens": 393, "sec": 8.704100847244263, "cost": 0.011605500000000001, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T23", "target": "smart_home_skill", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "**No**, you cannot retrieve the current volume level or mute status.\n\n### Why:\n* **Protocol limitation:** The integration communicates with the Samsung TV via the local Tizen WebSocket API (`wss://TV_IP:8002`), which does not support reading volume level or mute state.\n* **Status endpoint return value:** The status endpoint (`get_status`) only returns `PowerState`.", "error": null, "opened": [ "smart_home_skill" ], "requested": [ "smart_home_skill" ], "opened_target": true, "prompt_tokens": 12762, "completion_tokens": 463, "sec": 9.426921844482422, "cost": 0.01130775, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T24", "target": "skill_unifi", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "When running the UniFi controller observation CLI (`unifi-skill`):\n\n* **Exit Code:** **`10`** indicates an authentication failure (cookie expired).\n* **How to Resolve:** The session cookie must be manually refreshed by a human. Notify the user/human to refresh the session cookie according to the instructions in `docs/cookie_refresh.md` (exporting a fresh session cookie and updating the path/configuration in the local `.env` file). It is strictly a human step, as the tool does not perform mutations or direct interactive logins.", "error": null, "opened": [ "skill_unifi" ], "requested": [ "skill_unifi" ], "opened_target": true, "prompt_tokens": 12643, "completion_tokens": 225, "sec": 9.494213104248047, "cost": 0.010326, "web_tools": [], "n_calls": 2, "nudged": false }, { "id": "T25", "target": "asr", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "The maximum duration allowed for a single audio file is **7,200 seconds (2 hours)**.\n\nIf an audio file exceeds 7,200 seconds, the pipeline rejects it before any model loads. \n\nKey details to keep in mind when handling long recordings:\n- **Splitting files:** You can split recordings longer than 2 hours into smaller parts (under 7,200 seconds each). Note, however, that speaker labels (acoustic identifiers) do not automatically carry over across separate slice boundaries.\n- **Batch totals:** The limit applies strictly per individual file—having multiple files in a batch that collectively exceed 2 hours is supported, as long as no single file exceeds 7,200 seconds.", "error": null, "opened": [ "asr" ], "requested": [ "asr" ], "opened_target": true, "prompt_tokens": 13825, "completion_tokens": 658, "sec": 9.94173288345337, "cost": 0.01283625, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T26", "target": "skill_outlook", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "When using `calendar invite` or `calendar update` without specifying the `--timezone` argument, the default timezone used is **`UTC`**.\n\nImportant details to keep in mind:\n- `--start` and `--end` must be wall-clock times without UTC offsets (e.g., `YYYY-MM-DDTHH:MM:SS`).\n- In `calendar update`, the timezone defaults to `UTC` and does **not** inherit the existing event's timezone. Always provide an explicit `--timezone` (accepts IANA names like `America/Los_Angeles` or common aliases like `PT`, `ET`, etc.) if the meeting is not meant to be in UTC.", "error": null, "opened": [ "skill_google_docs", "skill_outlook" ], "requested": [ "skill_google_docs", "skill_outlook" ], "opened_target": true, "prompt_tokens": 34076, "completion_tokens": 730, "sec": 10.808804273605347, "cost": 0.0282945, "web_tools": [], "n_calls": 3, "nudged": true }, { "id": "T27", "target": "skill_resend_email", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "### Why Did This Happen?\n\nIn the Resend API and CLI, the `--after` (and `--before`) flags are **pagination cursors**, not timestamp or date filters.\n\nWhen you pass an ISO 8601 timestamp string like `2023-10-01T00:00:00Z`, the API returns a validation error because it does not accept dates for pagination parameters.\n\n---\n\n### What Does the `--after` Flag Actually Expect?\n\nThe `--after` flag expects an **email ID (UUID)** representing a pagination cursor to fetch the next page of results after that specific record.\n\n---\n\n### How to Retrieve Emails by Time Window\n\nResend does not support server-side date range filtering on received emails. To inspect or analyze messages within a specific time window, fetch the messages first and filter them locally:\n\n1. **Export or list recent messages using a higher `--limit`:**\n ```bash\n .venv/bin/python -m resend_email_skill.cli received export-all-md \\\n --limit 100 \\\n --output-dir data/received/markdown \\\n --format json\n ```\n *(Or inspect via JSON output: `.venv/bin/python -m resend_email_skill.cli received list --limit 100 --format json`)*\n\n2. **Filter locally by timestamp:**\n - If using `export-all-md`, inspect the `created_at` field in each Markdown file's frontmatter.\n - If using `received list --format json`, parse the JSON array and filter items where `created_at >= \"2023-10-01T00:00:00Z\"`.", "error": null, "opened": [ "skill_resend_email" ], "requested": [ "skill_resend_email" ], "opened_target": true, "prompt_tokens": 14013, "completion_tokens": 1080, "sec": 12.577944993972778, "cost": 0.01455975, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T28", "target": "skill_firecrawl", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "How you set this up depends on which search CLI tool you are using:\n\n---\n\n### Option 1: Using `tavily_skill`\n\nThe Tavily CLI allows you to pass both `--include-domain` and `--exclude-domain` flags in the same command. You can repeat `--include-domain` for each target site and use `--exclude-domain` for the site you wish to omit:\n\n```bash\npython -m tavily_skill search \"your search query\" \\\n --include-domain github.com \\\n --include-domain docs.anthropic.com \\\n --exclude-domain example-excluded.com\n```\n\n---\n\n### Option 2: Using `firecrawl_skill`\n\nIn Firecrawl, `--include-domain` and `--exclude-domain` CLI flags are **mutually exclusive** (combining both flags will trigger an error). \n\nTo achieve the same behavior, use `--include-domain` for the targeted sites and inject the exclusion directly into the search query text using the standard `-site:` operator:\n\n```bash\npython -m firecrawl_skill search \"your search query -site:example-excluded.com\" \\\n --include-domain github.com \\\n --include-domain docs.anthropic.com\n```\n\n*(Alternatively, you can handle all domain filtering in the query string itself using `site:...` and `-site:...` operators).*\n\n---\n\n### Inspecting the Results\n\nFor both CLIs:\n1. The search results are saved to a JSON file on disk (by default in `./tmp//` or the respective output directory environment variable).\n2. The command outputs a lightweight JSON status object to stdout:\n ```json\n {\n \"command\": \"search\",\n \"status\": \"ok\",\n \"output_mode\": \"file\",\n \"output_path\": \"tmp/tavily/search_...json\"\n }\n ```\n3. Read the payload from the specific `output_path` returned in stdout to parse `data.results`.", "error": null, "opened": [ "skill_tavily", "skill_firecrawl" ], "requested": [ "skill_tavily", "skill_firecrawl" ], "opened_target": true, "prompt_tokens": 23900, "completion_tokens": 1344, "sec": 13.373828887939453, "cost": 0.022965, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T29", "target": "video-scoring-audio", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "You should avoid downmixing stereo audio to mono with `ffmpeg -ac 1` before measuring the peak level because **ffmpeg's downmixing can artificially boost correlated signals by approximately +3 dB**, leading to false reports of clipping.\n\n### The Mechanism and Consequence\n- **Correlated channel summing:** In many mixes (such as mono voice-overs or center-panned audio duplicated across left and right channels), the left and right channels are identical or strongly correlated. When ffmpeg sums them together during downmixing to a single channel, constructive interference increases the level by roughly +3 dB.\n- **False clipping alarms:** Because of that gain increase, a mix with a safe true peak (e.g., -1.5 dBFS / peak < 1.0) can artificially spike above 1.0 (for instance, reading 1.19) and appear to be clipping when the original stereo channels are actually within spec.\n\n### Correct Approach\n- **Measure across all discrete channels:** Inspect peak and clipping directly across each individual channel without downmixing first (e.g., computing `np.max(np.abs(xc))` over the raw multichannel buffer).\n- **Independent cross-check:** If you need a reliable integrated loudness and true peak verification using ffmpeg directly on the stereo file, run:\n ```bash\n ffmpeg -af ebur128=peak=true -f null -\n ```", "error": null, "opened": [ "video-scoring-audio" ], "requested": [ "video-scoring-audio" ], "opened_target": true, "prompt_tokens": 17182, "completion_tokens": 704, "sec": 11.546525716781616, "cost": 0.015526500000000002, "web_tools": [], "n_calls": 2, "nudged": true }, { "id": "T30", "target": "skill_playwright_test", "model": "gemini-3.8-flash", "condition": "index_rep2", "answer": "This issue is caused by a **stale browser session profile / user-data directory containing expired or invalid session tokens from a previous session**.\n\n### Root Cause Mechanism\n\n1. **Token Detection**: When the browser loads the page, the SSO SDK inspects browser storage (cookies, `localStorage`, or `sessionStorage`) from the existing profile and detects a leftover session/token.\n2. **Initial Request & 401**: The client SDK assumes the user has an existing session and attempts to authenticate against the backend or SSO provider using that stale token. The backend rejects it with an HTTP `401 Unauthorized`.\n3. **Automatic Refresh/Retry Loop**: Upon receiving a 401, the SDK transitions to \"signing in\" to trigger a refresh/re-authentication attempt.\n4. **Failure & Oscillation**: Because the stored credentials/refresh token are invalid or out of sync, the re-authentication attempt fails again with another 401. The SDK switches back to \"loading\" and retries, creating an infinite redirect/state oscillation loop.\n\n---\n\n### How to Fix It\n\n#### 1. Clear the Stale Profile (Immediate Fix)\nIf running a dedicated debugging/CDP Chrome instance (e.g., using `--user-data-dir`):\n- Stop the Chrome process.\n- Remove the profile directory completely before launching:\n ```bash\n rm -rf /tmp/pw_debug_profile # or your specific user-data-dir path\n ```\n\n#### 2. Isolate Browser Contexts in Automation\nIf using Playwright, Puppeteer, or similar test frameworks:\n- Avoid launching Chrome with a dirty persistent context or sharing storage across test runs.\n- Use fresh, isolated browser contexts for each auth test:\n ```python\n # Always create a new context to avoid inheriting stale session storage/cookies\n context = await browser.new_context()\n page = await context.new_page()\n ```\n\n#### 3. Manual Browser Clean-Up\nIf debugging in a standard browser window:\n- Clear all cookies, `localStorage`, and `sessionStorage` for both the application origin and the SSO identity provider domain, or test the flow inside an **Incognito / Private Browsing window**.", "error": null, "opened": [ "skill_session_keeper", "skill_playwright_test", "playwright_ajax_capture", "bestpractice_ai_debugging_diagnosis" ], "requested": [ "skill_session_keeper", "skill_playwright_test", "playwright_ajax_capture", "bestpractice_ai_debugging_diagnosis" ], "opened_target": true, "prompt_tokens": 21379, "completion_tokens": 1003, "sec": 11.72202205657959, "cost": 0.0197955, "web_tools": [], "n_calls": 2, "nudged": true } ]